Reviewerly/exHarmony huggingface dataset:
An Online Neonatal Intensive-Care Unit Monitoring System for Hospitals in Nigeria

This paper presents an online monitoring system for the storage and retrieval of physiological data from neonates admitted into the Neonatal intensive care units (NICU) of Obafemi Awolowo University Teaching Hospital, Ile-Ife, Nigeria. In order to develop this system, the requirements of the proposed system were identified and analyzed as system and user requirements independently and the requirements were designed using the Unified Modeling Language (UML) tools. The system was implemented using Web 2.0 technologies such as, the hypertext markup language (HTML), the cascading styling sheets (CSS), PHP and MySQL. With the system, storage and retrieval of information by the nurses and any authorized users will be easy.
A Sentinel-Based Peer Assessment Mechanism for Collaborative Learning

This paper introduces a novel mechanism to improve the performance of peer assessment for collaborative learning. Firstly, a small set of assignments which have being pre-scored by the teacher impartially, are introduced as “sentinels”. The reliability of a reviewer can be estimated by the deviation between the sentinels’ scores judged by the reviewers and the impartial scores. Through filtering the inferior reviewers by the reliability, each score can then be subjected into mean value correction and standard deviation correction processes sequentially. Then the optimized mutual score which mitigated the influence of the subjective differences of the reviewers are obtained. We perform our experiments on 200 learners. They are asked to submit their assignments and review each other. In the experiments, the sentinel-based mechanism is compared with several other baseline algorithms. It proves that the proposed mechanism can effectively improve the accuracy of peer assessment, and promote the development of collaborative learning.
CARRIED BAGGAGE DETECTION AND CLASSIFICATION USING MULTI-TREND BINARY CODE DESCRIPTOR AND SUPPORT VECTOR MACHINE

Automatic video surveillance systems have gained significant importance due to an increase in crime rate over the last two decades. Automatic baggage detection through surveillance camera can help in security and monitoring in public places. A detection algorithm for humans (with or without carrying baggage) is proposed in this paper. Detection in the proposed method can be achieved by employing spatial information of the baggage of various texture patterns with locus to the human body carrying it. To extract the features of body parts (such as head, trunk and limbs), the descriptor is exhibited and trained by the support vector machine classifier. The proposed approach has been widely assessed by using publically available datasets. The experimental results have shown that the proposed approach is viable for baggage detection and classification as compared to the other available approaches.
ANALYSIS OF A UNIVERSITY TEACHER’S METHODOLOGICAL READINESS FOR DISTANCE LEARNING

The article is devoted to the description of the results of a study of the methodological readiness of a university teacher to organize the educational process by means of distance learning. The purpose of the research is to identify significant characteristics of the methodological readiness of a university teacher to use distance tools in connection with the coronavirus pandemic. The study involved two groups: the main and control.The main group is 319 people consisted of teachers from Kostroma State University. The control group is 42 people consisted of teachers from more than 10 universities of the Russian Federation. The study allowed us to come to the conclusions:most faculty support university management and find distance education effective; self-esteem of readiness to work in a remote format is high, but this requires an external examination; positive self-esteem correlates with positive expectations regarding student readiness and knowledge of a wide range of distance education tools;most teachers have negative expectations about the decline in the quality of distance education; teachers insufficiently use distance learning tools that provide communication between subjects of education, the means of monitoring and evaluating educational outcomes dominate; adaptation to the current situation coexists with the desire to enter the office mode of operation and abandon distance formats. The article suggests ways of methodological support for the teacher's work in a remote format: developing a universal feedback mechanism and assessing the quality of education in a certain discipline by students; development and implementation of personalized methodological support programs built on a network basis; encouraging teachers to use distance education tools that enable collaboration and communication in the educational process.
GPU Accelerated TOPSIS Algorithm for QoS Aware Web Service Selection

Web service selection is the method of selecting the best web service based on non-functional aspects. With proliferation of web services, there is a demand for efficient approaches to select the best web services from the available pool. A number of Multi criteria decision making approaches are adopted for QoS aware web service selection. The exploitation in web service deployment increases the computation power in executing web service selection. Graphics processing unit (GPU) can achieve reduction in computation power by performing multiple tasks at a time. The independent tasks in the algorithm are parallelized by utilizing GPU in order to achieve more efficiency. This works proves that GPU acceleration can be utilized in parallelizing TOPSIS algorithm for web service selection.
Block level time variant dynamic encryption algorithm for improved cloud security and de-duplication using block level topical similarity

The problem of security and memory management has been well studied. A number of approaches has been discussed for the data security in cloud environment. However, the method does not produce higher security and introduces higher data duplication in the cloud which affects the data management performance. To overcome these issues, an efficient block level encryption and de-duplication scheme has been presented in this paper. The method represents the data into a number of blocks and stores them in the cloud storage. For each block considered, a dynamic encryption/decryption keys has been generated at each time window. The user has been validated with different public/private key approach. For the data access, the algorithm generates different keys for different blocks which restrict the access of users. Based on the key being generated, the data has been encrypted by the system to be decrypted by the cloud user upon access. The method maintains a block key table which contains the keys to be used for the particular time window. The method produces higher efficient results on data security and improves the performance of data management.
MULTI-OBJECTIVES MEMETIC DISCRETE DIFFERENTIAL EVOLUTION ALGORITHM FOR SOLVING THE CONTAINER PRE-MARSHALLING PROBLEM

The Container Pre-marshalling Problem (CPMP) has the significant effect of reducing ship berthing time, and can help in increasing terminal turnover rate.In order to solve the CPMP, this research proposes a Multi-objectives Memetic Discrete Differential Evolution algorithm (MODDE).To date, existing research in CPMP only focuses on single-objective approaches.However, this is not a suitable approach due to the considerable effort required to validate the hard constraints of CPMP.Therefore, this work aims at addressing the effect of minimizing the number of miss-overlaid containers on the total number of movements in building the final feasible bay layout by embedding it in the multi-objectives evaluation function.The proposed algorithm combines the Discrete Differential Evolution mutation with the Memetic Algorithm evolutionary steps in order to find high quality CPMP solutions, achieve high convergence rate and avoid premature convergence and local optima problems.In addition, it improves the exploration and exploitation capabilities of the algorithm.The standard pre-marshalling benchmark dataset
Psychological factors in the development of the career potential of students

Т. И. Куликова, Е
Study on Occupancy Prediction for Building Operation using Machine Learning Method

Occupancy in buildings is an important input no matter to building design or building on-site operation.As for building design, the occupancy state is related to the heat gain in buildings and further equipment operation.With room occupied, the comfortable ambient parameter is required and the equipment in buildings are potential to be used.As for the building operation, with the detection of occupancy, the auto control is likely to perform.However, the accuracy needs improved.In this study, occupancy state is predicted by proposed machine learning method.During the occupancy prediction, the temporal sequential characteristic is considered to obtain a better prediction performance.The evaluation indicator is defined and the evaluation is conducted through ten fold cross validation.This study can be further promoted to building operation and performance simulation.
A Comparative Study of Facial Indices of Maharashtrian Adults with Other Ethnic Group and Test For Reliability of Sexual Dimorphism

Background: Variations in different craniofacial features is studied and documented using various anthropometric techniques.The same depends on gender, race, ethnicity, nutritional and socioeconomic status of a person.So the human face and its variations has always been an object of interest for anatomists, forensic experts, anthropologists, plastic surgeons, maxillofacial surgeons, security experts and artists.The aim of the present study was to identify the facial phenotypes of the Maharashtrian Males and Females.Availability of the Facial indices will augment the already available data of other studies. Materials and Methods:The present study was done on 505 (243 males, 262 females) pure bred Maharashtrian adults (3 generations on the maternal and paternal side of Maharashtrian origin)of the age group 18-60 years.The data was analyzed using MS excel Data Analysis Pak and Two-Sample t-test for Unequal Variances was done for facial indices of male and females to check for sexual dimorphism. Conclusion:The predominant facial type in Maharashtrian male is Mesoprosopic (37.86%) followed by Euryprosopic (29.22%) and that of female is Mesoprosopic (33.21%) and Euryprosopic (32.06%).This is similar to the results of studies on Indian males and females except Gujarati males who have Hypereuryprosopic face and Rajput males havingHyperleptoprosopic (long )face.A statistically significant difference (p< 0.05).was obtained between facial indices of Maharashtrian males and females, which is a key to sexual dimorphism
Real-Time Object Recognition using Region based Convolution Neural Network and Recursive Neural Network

The recognition of real-world objects demands the recognition and characterization of digital image samples. Automated methods for the detection and recognition of entity types have many significant commercial and industrial applications. While deep convolution neural networks (CNN) and machine learning (ML) concepts have contributed to the classification of globe items, they cannot fully scale the reliance of powerful GPUs to classify the key attributes of images. By using a Recurrent Neural Network (RNN) we tend to resolve the issue arisen in the previous systems. In particular, a hybrid approach using R-CNN and RNN has been proposed that improve the accuracy of object recognition and learn structured image attributes and begin image analysis. Specifically, we applied the transfer learning approach to pass the load parameters which were pre-trained on the Image web dataset to the RNN portion and follow a custom loss feature for the model to train and test more rapidly with precise weight parameters. Experimental results show that in comparison to CNN models like Resent, origin V3, etc., our proposed model achieved improved accuracy in categorizing universe pictures.
Conversational AI

As we know a business has many customers and these customers may have a lot of queries. It is hardly possible for a customer representative to attend every customer personally. Hence a conversational AI is used to provide a simple and efficient solution and provide a human like experience to the user. The project aims to develop a Conversation AI program that can help the business handle different types of queries the customers may have. Even people who know a little about a computer or a smart phone can easily use this AI. This product in saves the resources and time of the business and also eases the customers.
Environment installation

Installation of the environment is the first step in developing machine learning models. Python and TensorFlow are widely used programming languages and toolkits for building these models. This chapter first presents how to install Python, TensorFlow, and necessary expansion packages on both the Windows and Ubuntu operating systems. Next, it introduces the settings of Jupyter Notebook and PyCharm IDE for compiling the simple and complex network models, respectively. Finally, it presents how to download all the example programs of this book from GitHub and how to run the code examples to help familiarize the reader with using these toolkits.
Exploring Classification Techniques for Sentiment Analysis

In the recent years, there has been tremendous improvements in the Information Technology industry. Every day 2.5 quintillion bytes of data is generated, this provides sentiment analysis to become a key tool to make something out of it. This has allowed companies to grab this key acumen and automate the needed processes. This information can serve as good source for extracting useful insights regarding the products/services. It would be helpful for the product owners if these thousands of reviews could be summarized using the latest technologies. Also it would be more useful if the sentiment scores are provided for each aspects of products/services. This paper presents Sentiment Analysis for various predefined aspects for Hotel Reviews. We have used Naive Bayes Classifier for classifying hotel reviews as positive or negative and we have evaluated the performance and suggested few future enhancements.
Patent Translation

Goto, IsaoThe NTCIRPatent translation patent translationPatent translation task was the first task for the machine translation of patents that used large-scale patent parallel sentence pairs. In this chapter, we first present the history of machine translation; the contribution of evaluation workshops to machine translation research, and previous evaluation workshops; and the challenge of patent translationPatent translation at the time of the first patent translation task at NTCIR. We then describe the innovations at NTCIR, including the sharing of research infrastructure, the progress of corpus-based machine translation technologies, and evaluation methods for patent translation. Finally, we outline the developments in machine translation technologies, including patent translationPatent translation and remark on the future of patent translationPatent translation.
Metric Spaces

This chapter briefly summarizes the fundamental topological concepts relevant to functional analysis: open and closed sets, convergence, completeness, and continuity.
THE IMPORTANCE OF AWARENESS OF THE LIMITED RATIONALITY OF THE CONSUMER IN THE FORMATION OF FINANCIAL CULTURE SKILLS OF UNIVERSITY STUDENTS

The article is devoted to the formation of financial culture skills of university students based on the understanding of modern ideas about consumer behavior
USE OF MODULAR RATING SYSTEMS IN THE STUDY OF FOREIGN LANGUAGES

The present article deals with the question o f using module-rating system during foreign language learning process.
Present Situation and Enlightenment of Artificial Intelligence Lifelong Education System in Singapore

&lt;div&gt;&lt;p class="4"&gt;The development of artificial intelligence technology depends on the cultivation of artificial intelligence talents, so countries all over the world attach great importance to the cultivation of artificial intelligence talents. As a world power in science, technology and education, Singapore has taken the lead in the field of artificial intelligence education. Based on this, the article focuses on Artificial Intelligence Education in Singapore. Firstly, it introduces the lifelong education system of artificial intelligence in Singapore in detail, and then analyzes the characteristics of this system, including the curriculum system of teaching students according to their aptitude, the teaching method of doing things according to their needs, the teacher selection mechanism according to their needs, etc. Finally, based on the current situation of AI education in China, this paper analyzes the Enlightenment of AI education in Singapore to China from the perspectives of teaching resources, curriculum system and teacher team construction, so as to promote the development of AI education in China.&lt;/p&gt;&lt;/div&gt;
The used of deixis in “World Travel Magazine”

This research is entitled The Used of Deixis In “World Travel Magazine”. The aims of this research are to identify the types of deixis that used in “World Travel Magazine.” and its reference. The term Deixis comes from classical Greek for one of the most basic things we do with utterances. It means ‘pointing’ via language. The study in this research concerned with the way in which the researcher explains the kinds of deixis and the reference of each deixis which used in the article that published on World Travel Magazine using the theory of Pragmatics focusing on Deixis that purposed by Levinson 1983. Qualitative research was used as the method of the research. The method of collecting the data is documentation. The steps to analyze the data are reading the article, finding the deixis, and classifying the types of deixis.
Research on collaborative filtering recommendation algorithm based on temporal context

Collaborative filtering-based algorithms are widely used to make recommendations without analyzing the contents. Time effect can be seen everywhere in our daily life. User interests will change over time, so we use the time-decay function to integrate the user-item rating matrix and adjust it by different time-decay factors to optimize the model. And we conducted experiments using the improved algorithm on the movie evaluation dataset movielens-1 m. The results show that the algorithm is able to improve the accuracy and coverage of recommendations under specific time factors, and also can partly improve the recommendation efficiency.
La Práctica de la Pronunciación en Cursos de Formación de Profesores de ELE

In this study, we propose the elaboration of activities for the development of the pronunciation of Spanish as a foreign language for Brazilian students in Spanish teacher training courses. The proposal is based on the communicative teaching of pronunciation (CELCE-MURCIA et al ., 2010) and some difficulties concerning pronunciation evidenced in the classroom in a Language Course (BRISOLARA, 2011; BRISOLARA; SEMINO, 2016; BRISOLARA; MACHRY DA SILVA, 2020). The activities follow the five pedagogical steps by Celce-Murcia et al. (2010), that is, description and analysis, listening discrimination, controlled practice, guided practice, and communicative practice. In addition, for the selection of consonant segments worked in the activities, we considered the similarities and differences between Brazilian Portuguese – the learners’ mother tongue – and Spanish – a foreign language.
A Domain-Specific Language for generating tasks from Programs Source Code

Approaches to task generation for various disciplines are discussed, mentioned that a parameter method cannot be applied to programming. The authors developed an application of the idea of a constructive-selective method for creating programming tasks. A task created for constructive-selective methodical contains a set of components, from which a student makes up his answer (a program). Application of this method allows diversifying of tasks in programming and creates a new way of assessment based on the content of the answer. It’s also allows to grade both fully and partially correct answers. The main concepts of the model for representing programming tasks are illustrated by examples. The domain-specific markup language and its translator that allows generating tasks from a teacher’s program source text are proposed. An example of a translator usage is given.
Acid, Salt, Heat, Pressure: Testing the Limits of Biomolecule Preservation

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Acid, Salt, Heat, Pressure: Testing the Limits of Biomolecule PreservationAuthorsArdithBravenecSee all authors Ardith BravenecCorresponding Author• Submitting AuthorThe University of Washingtonview email addressThe email was not providedcopy email address
Thermodynamics of quantum switch information capacity activation

We address a new setting where the second law is under question: thermalizations in a quantum superposition of causal orders, enacted by the so-called quantum switch. This superposition has been shown to be associated with an increase in the communication capacity of the channels, yielding an apparent violation of the data-processing inequality and a possibility to separate hot from cold. We analyze the thermodynamics of this information capacity increasing process. We show how the information capacity increase is compatible with thermodynamics. We show that there may indeed be an information capacity increase for consecutive thermalizations obeying the first and second laws of thermodynamics if these are placed in an indefinite order and moreover that only a significantly bounded increase is possible. The increase comes at the cost of consuming a thermodynamic resource, the free energy of coherence associated with the switch.
Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention

The massive amount of trainable parameters in the pre-trained language models (PLMs) makes them hard to be deployed to multiple downstream tasks.To address this issue, parameter-efficient transfer learning methods have been proposed to tune only a few parameters during fine-tuning while freezing the rest.This paper looks at existing methods along this line through the kernel lens.Motivated by the connection between self-attention in transformer-based PLMs and kernel learning, we propose kernel-wise adapters, namely Kernel-mix, that utilize the kernel structure in self-attention to guide the assignment of the tunable parameters.These adapters use guidelines found in classical kernel learning and enable separate parameter tuning for each attention head.Our empirical results, over a diverse set of natural language generation and understanding tasks, show that our proposed adapters can attain or improve the strong performance of existing baselines.
Символика снов и сновидений в контексте интимной психодрамы Тараса Шевченко (попытка архетипического анализа)

The motifs of dreams appear in all literary works written by Taras Shevchenko. They are present, for example, in several lyric poems and fragments of Journal, reflecting important moments in the poet’s intimate psychodrama. The archetypal analysis of their psycho-symbolic content allows one to reveal the specificities of the poet’s inner life, as well as to trace the patterns of animatic projections of his individual unconscious into external reality.