Reviewerly/exHarmony huggingface dataset:
A Conceptual Framework for Applying Blockchain Technology in Procurement Processes

Recently, new cutting-edge technologies such as Internet of Things (IOT), Artificial Intelligence, Big Data Analysis and Blockchain Technology (BCT) have been rapidly introduced in various industries. Among them, the BCT is expected as one of the most useful technologies in the field of procurement. The characteristics of BCT are 1) Information distribution and synchronization, 2) P2P networks and consensus, 3) Smart contract and payment and 4) Data immutability. These characteristics of BCT could be well applied into each segment of procurement processes including finding potential suppliers, negotiation and contract management, order and fulfillment management and supplier management. Even though the possibility of successful implementation of BCT into overall procurement processes could be expected very high, it has been rarely considered in the literature. In this study, a conceptual framework for applying BCT into procurement processes is proposed. Two real world cases of BCT implemented in procurement processes are also introduced.
Detecting Duplicate Questions in Community Based Websites Using Machine Learning

Searching answers to the queries is easy nowadays because of the most popular community based websites like Quora and stack overflow. These websites maintain Q&A database which consists of millions of questions with answers. But the practical problem with these databases is the generation of duplicate questions. Users are generating duplicate questions at a higher in spite of providing clear guidelines for the users to search for the question before posting any new question. Developing an automatic method for duplicate question detection is an on-going research from many years. In this paper we have made an effort to develop such a method using machine learning techniques such as Logistic regression, decision tree, decision tree with baggingrandom forest and Adaboost classifier. We were able to achieve impressive results with 79.21%, 79.29%, 81.70%, 81.72% and 81.73% of classification accuracy from the mentioned machine learning algorithms respectively. The results were compared with the similar study in the state of the art literature. Our results showed good performance in the comparison.
Secuencias didácticas gamificadas por docentes de LE en formación continua: puntos, insignias y tablas de clasificación

espanolEn este articulo analizamos la implementacion de los elementos propios de la gamificacion superficial para descubrir con que tipos de actividades se asocian y como se determina su obtencion. Los resultados muestran que la gamificacion superficial esta muy presente, en relacion con el uso de los puntos, aunque tambien se observa frecuentemente el uso de insignias y tablas de clasificacion. Asimismo, vemos que los alumnos consiguen los puntos a partir de la realizacion de actividades sobre el sistema de la lengua, pero mas en actividades con las que se fomenta el desarrollo de las habilidades comunicativas. EnglishThe aim of this paper is to analyze the implementation of thin-layer gamification elements to find out with which activities they are associated and how they are obtained. The results show that thin-layer gamification can be found in relation with the use of points, although badges and leaderboards are also used. The analysis shows that the students get points not only from activities revolving around language as a system but from activities where communicative skills are fostered
Deep Q-Learning for Trading Cryptocurrency

This article sets forth a framework for deep reinforcement learning as applied to trading cryptocurrencies. Specifically, the authors adopt Q-Learning, which is a model-free reinforcement learning algorithm, to implement a deep neural network to approximate the best possible states and actions to take in the cryptocurrency market. Bitcoin, Ethereum, and Litecoin were selected as representatives to test the model. The Deep Q trading agent generated an average portfolio return of 65.98%, although it showed extreme volatility over the 2,000 runs. Despite the high volatility of deep reinforcement learning, the experiment demonstrates that it has exceptionally high potential to be employed and provides a solid foundation on which to build further research. <b>TOPICS:</b>Currency, big data/machine learning, performance measurement <b>Key Findings</b> ▪ The authors use deep neural networks to create a Deep Q-Learning trading agent that approximates the best actions to take based on rewards to maximize returns from trading the three cryptocurrencies with the largest market capitalization. ▪ The Deep Q-Learning agent generates a return of 65.98% on average over the course of 2,000 episodes; however, the returns do exhibit a large standard deviation given the highly volatile nature of the cryptocurrencies. ▪ The authors introduce a framework on which future deep reinforcement learning and rewards-based trading agents can be built and improved.
Data Management Tools to Measure the Impact of Core Facilities

is a research core facility that supports the study of biomolecules with an emphasis on protein structure and function, molecular interactions, and imaging.The mission of the core is to facilitate access to instrumentation that might otherwise be unavailable because of the cost, training for new users, and scientific staff with specialized skills to support early-stage investigators, as well as more established senior investigators.Data collection and management of users and their research output is essential to understand the impact of the center on the research environment and research productivity.However, challenges are often encountered when trying to fully quantify the impact of a core facility on the institution, as well as on the career success of individual investigators.This challenge can be exacerbated under the conditions of unprecedented growth in biomedical research and shared core facility use that has been experienced at Boise State University, an institution of emerging research excellence.Responding to these challenges required new approaches to information management, reporting, assessment, and evaluation.Our specific data management, evaluation, and assessment challenges included 1) collection and management of annual reporting information from investigators, staff, and students in a streamlined manner that did not lead to reporting fatigue; 2) application of software for analyzing synergy among programs' management strategy and investigator success; and 3) consolidation of core facility management, billing, and reporting capabilities into 1 cohesive system.The data management tools adopted had a beneficial effect by saving time, reducing administrative burden, and streamlining reporting.Practices implemented for data management have facilitated effective evaluation and future program planning.The substantial burden of assessment requirements necessitates early consideration of a strategy for data management to allow assessment of impact.
Aplikasi Multimedia Untuk Karaoke Online

DeJava Karaoke is a family karaoke place in the city of Pematang Siantar. During the pandemic, DeJava Karaoke needed a solution to the cessation of business activities due to government policies. An online-based karaoke system can be the right solution, because customers can carry out karaoke activities from home. Customers can register to register as a member at a fee that can be set by DeJava Karaoke. After becoming a member, features that can be accessed are playing karaoke videos, search videos, request videos, hide vocals and make playlists. The test results on the system using the blackbox testing method prove that the system's functionality has been running according to the predetermined design. The online karaoke system is expected to be an opportunity for DeJava Karaoke to continue running business activities during a pandemic.
Personality Development Potential of a Literary Text in Digitalized Teaching of RFL

The relevance of using personality development potential of literary texts in order to form the necessary complex of knowledge about the most significant spiritual and moral values of the Russian people is due to the situation of a complete transition with no alternatives to teaching in a distance format at the preparatory faculty.The article examines the experience of using the potential of the texts from Russian classical literature for personal self-development and self-realization of foreign students at Russian as a foreign language lessons (pre-university stage) in conditions where direct immersion in the language is impossible.Such literary works allow foreign students to begin their acquaintance with Russian history and culture, to form basic ideas about the values of Russian society and the mentality of Russian people, to understand the degree of similarity and difference between Russian and native cultures, which generally contribute to their adaptation to Russian reality and integration into the educational multicultural space of the university.The approaches, the universal and specific requirements for the formation of an electronic educational course in RFL are presented.The course can be accessed in "Electronic information and educational environment of SWSU.SWSU's Courses" on Moodle platform.The platform thanks to its functional capabilities, allows to present educational material in various forms, to vary the methods of students' independent educational activities, to organize the necessary audiovisual support in mastering the Russian language within the elementary, basic and first certification levels (A1, A2, B1).
Identifying a Motivational Profile for Older Adults Towards Increased Physical Activity

Personalizing behavior change (BC) strategies to motivate increased physical activity is especially important for the diverse older adult population. However, there is a lack of knowledge about how to profile older users to most effectively personalize BC solutions. Self-awareness and social awareness are BC strategies commonly used in commercially available applications to promote physical activities. Through a randomized controlled trial (N = 53), we studied the effect of some personal factors on the physical activity of older adults under these two strategies. For this purpose, each BC strategy was implemented in a mobile application. Based on the statistical analysis of the measured step data and the collected questionnaire data, we identified a list of personal factors to personalize each BC strategy towards improved physical activity. Hereby we suggest how to create effective motivational profiles and provide design recommendations to personalize these BC strategies toward increased physical activity for older users.
Multi Task Learning based Framework for Multimodal Classification

Large-scale multi-modal classification aim to distinguish between different multi-modal data, and it has drawn dramatically attentions since last decade.In this paper, we propose a multi-task learning-based framework for the multimodal classification task, which consists of two branches: multi-modal autoencoder branch and attention-based multi-modal modeling branch.Multi-modal autoencoder can receive multi-modal features and obtain the interactive information which called multi-modal encoder feature, and use this feature to reconstitute all the input data.Besides, multimodal encoder feature can be used to enrich the raw dataset, and improve the performance of downstream tasks (such as classification task).As for attention-based multimodal modeling branch, we first employ attention mechanism to make the model focused on important features, then we use the multi-modal encoder feature to enrich the input information, achieve a better performance.We conduct extensive experiments on different dataset, the results demonstrate the effectiveness of proposed framework.
A mixture varying-gain dynamic learning network for solving nonlinear and nonconvex constrained optimization problems

Nonlinear and nonconvex optimization problem (NNOP) is a challenging problem in control theory and applications. In this paper, a novel mixture varying-gain dynamic learning network (MVG-DLN) is proposed to solve NNOP with inequality constraints. To do so, first, this NNOP is transformed into some equations through Karush–Kuhn–Tucker (KKT) conditions and projection theorem, and the neuro-dynamics function can be obtained. Second, the time varying convergence parameter is utilized to obtain a faster convergence speed. Third, an integral term is used to strengthen the robustness. Theoretical analysis proves that the proposed MVG-DLN has global convergence and good robustness. Three numerical simulation comparisons between FT-FP-CDNN and MVG-DLN substantiate the faster convergence performance and greater robustness of the MVG-DLN in solving the nonlinear and nonconvex optimization problems.
Importance of Oral Hygiene and Maintaining Oral Health in Persons With Disabilities

This paper aims to approximate and facilitate the routine of daily oral hygiene for persons with disabilities, as well as to emphasize the importance of educating persons with disabilities and their caregivers about oral health as an essential part of overall health. Desk research of electronic databases was conducted with the aim of writing this paper, using the following keywords: ‘oral hygiene’, ‘dental plaque’, ‘oral health’ and ‘persons with disabilities’. Literature research has shown that persons with disabilities have poor oral hygiene, as well as that there is a lack of education among them and their caregivers about the importance of oral health and proper oral hygiene. Poor oral hygiene can affect a person’s quality of life due to discomfort during eating, bad breath, poor self-esteem, pain, and disturbed sleep, which is a result of caries or other diseases of the oral cavity. Maintaining oral health is an essential part of overall health.
PENGARUH PEMBELAJARAN ONLINE DI TENGAH PANDEMI COVID-19 TERHADAP HASIL BELAJAR TEMA 9 KELAS V SD NEGERI CEMPAKA 01 TEGAL

This study was conducted to determine the effect of online learning in the Covid-19 pandemic on learning outcomes in the 9th-grade theme of SDN Cempaka 01 Tegal. Type of quantitative research. The population in the study were all fifth-grade students of SD N Cempaka 01 Tegal. The research method used is a survey method. Singarimbun (2011: 3) explains that research with this survey method is a study that takes a sample from a population and uses a questionnaire as the main data collection tool. The questionnaire is a data collection tool that is done by giving a set of questions or written statements to the respondent to answer. The results showed that there was a positive influence on student learning outcomes. This is evidenced by the results of the analysis showed that the calculation of the product-moment correlation (rxy) between online learning (X) and learning outcomes (Y) obtained a correlation coefficient of 0.570. The coefficient r table with a significance level of 5% and N = 18 is 0.468. These results indicate that rcount is greater than rtable so that Ho is accepted. It can be concluded that there is a positive and significant influence between online learning in the Covid 19 Pandemic and the learning outcomes of Class V students at SDN Cempaka 01 Tegal.
The emerging role of artificial intelligence in the reporting of prostate pathology

Artificial intelligence (AI) has emerged as a promising tool for assisting pathologists in their diagnostic work. Several recent studies have shown encouraging results in the diagnosis and grading of prostate cancer on needle biopsies and AI has the potential to improve quality control, standardisation of procedures and the reduction of both interobserver variability and diagnostic errors.
A New Semantic Approach to Improve Webpage Segmentation

Webpage analysis is carried out for various purposes such as webpage segmentation. The goal of webpage segmentation is to divide a page into blocks that have similar elements. A fusion approach that combines different analyses is required in order to obtain high segmentation accuracy. In this paper, we propose a new fusion model for webpage segmentation, where we (1) merge webpage content into basic-blocks by simulating human perception; and, (2) identify similar blocks using semantic text similarity and regroup these similar blocks as fusion blocks. This approach is applied to three public datasets and evaluated by comparing with state-of-the-art algorithms. The results characterize that our proposed approach outperforms other existing webpage segmentation methods, in terms of accuracy.
Azure Well-Architected Framework: What and Why?

With the digital transformation of the modern world, the cloud real estate of organizations has expanded beyond expectation. Be it big data services, analytics, machine learning, or AI, everything has found a home in the cloud. With this transformation, it has become a challenge for architects to choose the best-suited architectures for each of these workloads. There are many aspects to be considered, starting from designing and deploying the applications to keeping the lights on at an optimal cost. Azure, being one of the leading cloud service providers, offers the Azure Well-Architected Framework to support organizations.
When Learning Is Continuous

Artificial intelligence and machine learning (AI/ML) medical devices are able to optimise their performance by learning from past experience. In healthcare, such devices are already applied within controlled settings in image analysis systems to detect conditions like diabetic retinopathy, for instance. In examining the regulatory governance of AI/ML medical devices in the United States, it is argued that the development and application of these devices as a technical and social concern whether in research or in clinical care must proceed in tandem with their identities in regulation. In the light of emerging regulatory principles and approaches put forward by the International Medical Device Regulators Forum, and endorsed by the US Food and Drug Administration, conventional thinking about clinical research and clinical practice as distinct and separate domains needs to be reconsidered. The high connectivity of AI/ML medical devices that are capable of adapting to their digital environment in order to optimise performance suggest that the research agenda persist beyond what may be currently limited to the pilot or feasibility stages of medical device trials. If continuous risk-monitoring is required to support the use of software as medical devices in a learning healthcare system, more robust and responsive regulatory mechanisms are needed, not less.
Local assignability of Lyapunov exponents of linear discrete-time system

We consider sufficient and necessary conditions for the proportional local assignability of the Lyapunov spectrum of the system $$x(m+1)=\left(A(m)+B(m)U(m)\right)x(m), \quad m\in\mathbb Z,\quad x\in\mathbb R^n.$$ The properties of stability of the Lyapunov spectrum and integral separation of linear discrete-time systems are studied, description of the spectral set of a linear system in the case of the full spectrum stability is obtained, the property of uniform complete controllability of a linear system with discrete time is studied, and the properties of the Bebutov shell of a linear discrete-time control system are investigated.
Ternary compound ontology matching for cognitive green computing

<abstract> Cognitive green computing (CGC) dedicates to study the designing, manufacturing, using and disposing of computers, servers and associated subsystems with minimal environmental damage. These solutions should provide efficient mechanisms for maximizing the efficiency of use of computing resources. Evolutionary algorithm (EA) is a well-known global search algorithm, which has been successfully used to solve various complex optimization problems. However, a run of population-based EA often requires huge memory consumption, which limited their applications in the memory-limited hardware. To overcome this drawback, in this work, we propose a compact EA (CEA) for the sake of CGC, whose compact encoding and evolving mechanism is able to significantly reduce the memory consumption. After that, we use it to address the ternary compound ontology matching problem. Six testing cases that consist of nine ontologies are used to test CEA's performance, and the experimental results show its effectiveness. </abstract>
Basketball-51: A Video Dataset for Activity Recognition in the Basketball Game

In recent years, there has been an increase in the association of technology in sports and live sports broadcasting networks. From score updates, broadcasting commercials, assisting referees for decision making, and minimizing errors, the adoption of technology has been used for fair play and improve results. This has been possible with the advancement in video analysis, classification techniques, and the availability of resources. This paper introduces a new labelled video dataset collected from a live basketball game broadcasted on live TV to determine the type of basket scored in the basketball game. Among different shots, the points the player can score are basically of three types: 3 points, 2 points, which depends on the range of shots taken and 1 point which is the free shots taken after a foul. This dataset consists of labelled video clips collected from the live broadcast of the game from the broadcasting medium to classify different scoring activities. This paper also gives the preliminary analysis of the dataset for different class labels using 3D ConvNet and two-stream 3D ConvNet methods to show the complexity of the dataset.
A classification-based fuzzy-rules proxy model to assist in the full model selection problem in high volume datasets

Improvement of accuracy in classifiers is a crucial topic in the machine learning field. The problem has been addressed, making new algorithms and selecting the fittest classifier for a given dataset. The latter approach combined with feature selection and pre-processing form up a new paradigm known as Full Model Selection. This paradigm is like a black box whose input is a dataset, and as an output, a precise classification model is obtained. Despite that, full model selection is not the first alternative with the larger datasets of nowadays. We propose the use of MapReduce to deal with huge datasets, a bio-inspired optimisation algorithm and the use of a novel algorithm based on fuzzy classification rules as a proxy model to guide the optimisation process. To the best of our knowledge, this work is the first to propose a classification algorithm based on fuzzy rules as a proxy model. Obtained results showed an accuracy improvement and a considerable reduction of the computing time in datasets of a wide range of sizes.
A Critical Review of Data Security in Cloud Computing Infrastructure

Cloud computing is a newly evolved technology for complex systems that allow on‐ demand, flexible, scalable, and low cost services with massive-scale services sharing among plentiful users. The wide receiver of the cloud computing idea has brought about significant effects in both fixed and mobile communication systems prompting frontline research to give fitting system protocols and network architecture, alongside resource administration/management components. In cloud computing, access control and security are two major problems. Therefore, Security of both services and users is a substantial issue for the uses and trust of the cloud computing. This paper audits recent works concentrating on security issues, solutions, and difficulties in cloud computing infrastructure.