Reviewerly/exHarmony huggingface dataset:
IOT Based Remote Livelihood Monitoring For Elderly People Care Assessment

This paper deals with livelihood monitoring and care assessment of old age people based on internet of things. The usage of electrical and non-electrical home appliances as well as health conditions are monitored by the sensors such as electrical sensor, force sensor, temperature sensor and contact sensor. All sensors are integrated into a common sensor node and sensor data are analyzed to predict wellness parameters. Based on the wellness parameters, the daily activities of elderly people are classified as neither normal nor abnormal. In case of abnormal condition, the automatic alert will be given to nearby hospitals and local care takers for immediate medical assistance. Additionally, the well-being conditions are periodically updated in IoT cloud for real time monitoring. Experiment was conducted and the performance was tested by the prediction of well-being conditions based on usage and non-usage of home appliances. From results, it is inferred that the proposed approach suffices old-age community to lead safe life without fear.
A trust-aware micropayment system for data muling in cloud environment: a Blockchain based approach

The rapid growth of the Internet of Things (IoT) and the cloud will enable new monitoring areas. More specifically, the integration of the Unmanned Aerial Vehicle (UAV) cloud with the sensor cloud for the purpose of data collection from an infrastructure-less area is of great interest. In this paper, we propose a trust-aware micropayment system based on the blockchain (BC) technology. A set of sensor cloudlets is used for monitoring a given area and a set of cloudlets of UAV s are employed for delivering the collected data to the Transactional Center (TC). We provide a micropayment protocol where the global amount of money is converted into a number of tokens, and the actors pay each others by employing the same coins. Moreover, a trust model is proposed for both the transactional center and the UAV broker. Through our trust-aware micropayment system, authentication, trustworthiness, double-spending prevention, data-duplication prevention, forging prevention, and traceability of all the operations of payment, are ensured. We conduct a simulation to validate our provided micropayment protocol.
Borealis Sound an Interactive Wall for Situational Awareness: The Impact of Responsive Architecture on Users

This paper refers to the presence of visual communication elements in public spaces using interactive multimedia surfaces. The state of the art, the development of a proposal, and its evaluation are presented. The interactive surface applies to school of music “Escola de Música do Conservatório Nacional,” to which a design project was done in an academic context. The proposal aims to explore the incorporation of digital multimedia surfaces enhancing new dynamics while users walk in the interior of the building. Visual content will be displayed, and the interaction between users and the multimedia surfaces is done by the sounds produce by people and instruments. To assess the intuitive nature and relevance of the proposal, satisfaction and usability tests were conducted with the potential users. It was concluded that users appreciated the proposal and was able to perceive the effect people’s presence have in the interaction with the multimedia surface.
Collective State Implementation on Particle Swarm Opimization for Feature Selection

The difficulty of choosing the right set of features increases as the number of features increases. Nature-inspired optimizers like genetic algorithm, binary particle swarms have proven to be the best for feature selection. However, binary particle search optimization has limitations of premature and slow convergence. Hence, a new collective state implementation for particle swarm optimization is proposed in this work. The proposed method is validated with five benchmark datasets. To measure the impact of the algorithm, our proposed solution was compared against two popular feature selection algorithms Binary Particle Swarm Optimization (BPSO) and Genetic Algorithm (GA). Our results show that our proposed solution Collective State Implementation-Particle Swarm Optimization (CSI-BPSO) achieves competitive scores in feature selection.
Novel method for liver cancer detection using super pixel and ANN algorithm

Abstract Nowadays most of the people are affected by liver cancer that can be the leading cause of cancer death. To analyze the liver cancer detection using super pixel segmentation and artificial neural network (ANN) Algorithm and it reduces the time complexity. An automatic 3D segmentation method for high energy sound waves in ultrasound liver images, which is based on a fusion of accurate time-consuming method and statistical texture prior. Three orthogonal linear filter banks are used to extract texture features from each image in the region around the point of the database. The atlas database includes registered liver images from previous patients and their segmented liver surfaces. To train the problem of patient–specific frequency content in the image features from the atlas database are using ANN algorithm and then to segment the super pixel segmentation the liver image from a new patient. The dataset of liver cancer detection are benign and malign.
ICT and Information Asymmetry New Evidence of the Financial System in Selected MENA countries

The present study aims to investigate the role of ICT in reducing the effect of information asymmetry on the financial development. For this purpose, data associated with the selected countries of MENA during the years 2004-2015 were extracted from the official sources and then the research model was evaluated in the short- and long-term using the dynamic generalized method of moments and FMOLS methods, respectively. The results show that, first, in the short and long terms, the greater the information asymmetry, the lower the financial development. Second, among the ICT indicators, only mobile internet can improve the financial system in the countries under study in the short term through information symmetry. Fixed telephone and fixed broadband internet cannot mitigate the negative effect of information asymmetry on short-term and long-term financial development in the countries under study.
FACTORS OF PSYCHOLOGICAL READYNESS FOR REAL ESTATE ACTIVITY

The topic's relevance is related to the situation in the labor market, which needs competent realtors. The aim is to study and investigate the main factors of the psychological readiness towards realtor professional activity. The article considers the main approaches to studying the phenomenon of psychological readiness and factors of its functioning. Psychological readiness for real estate activity is an integrated quality of personality, which aims to an adequate reaction to the possibilities of a situational solution of universal professional problems of a realtor utilizing acquired professional competencies. The study involved 130 people. The methods employed in the present study include various types of questionnaires, namely Guilford's Test (J. Guilford); Big Five / 5PFQ (P. Costa &amp; R. McCrae), Gertsberg Test (F. Hertsberh); Test Diagnostics of personality's socio-psychological attitudes in the motivational-needs sphere (O. F. Potomkin); Schwartz value survey (S. Schwartz); Professional activity motivation (K. Zamfir in A. Rean's modification); Diagnostics of motivational orientations in interpersonal communications (I. Ladanov, V. Urazaieva); Study of volitional self-regulation (A. Zvierkov, E. Eidman).The factors of psychological psychological readiness for professional activity as realtors were revealed: "standardness", "humanism", "pragmatism", "altruism", "endurance", "power", and "practicality". The main characteristics (competencies) of a specialist realtor include: a) the ability to solve complex problems and problems in the process of work (integral competence); b) the ability to apply knowledge in practical situations; ability to generate new ideas; ability to identify, formulate and solve problems; appreciate and respect diversity and multiculturalism; ability to act on the basis of ethical considerations (motives); ability to act socially responsibly and consciously; ability to develop and manage projects; ability to motivate people and move towards a common goal (general competencies); c) the ability to identify, analyze and interpret the information series of the realtor profession, identify problems and suggest ways to solve them; the ability to demonstrate an understanding of the patterns and features of human functioning in the context of the transaction in the context of professional tasks; ability to offer creative ways to solve professional problems, to make professionally correct decisions and to implement them (special competencies).
Evolving Neural Networks for Prediction with Negative Correlation Search: Application in Consumer Demand Forecasting

Neuroevolution is a powerful approach for learning neural networks for a large variety of machine learning applications. This paper describes a new approach that uses Negatively Correlated Search (NCS) to extend the basic NEAT neur. This approach uses NCS as a means for learning a population of negatively correlated neural network solutions, meaning that the individuals in the population are suited for performing well in different kinds of problem cases (or examples). An empirical evaluation of the proposed methodology we term NCS-NEAT leads to improved performance over basic NEAT in terms of accuracy and problem specific criteria and is a promising way to scale neuroevolution up to handle larger datasets without placing restrictions on the topology neural networks that are able to be learned. Two different machine learning problem classes were selected to be representative of a broad range of applications of deep learning and neuroevolution: the first is a price forecasting problem to predict the prices of mobile phones based on their characteristics; the second is a reinforcement learning test problem to validate the novel approach in a different type of problem.
Connective Potential Information for Collectively Interpreting Multi-Layered Neural Networks

This paper aims to propose a new type of information-theoretic method to make collective weights, obtained by compressing all connection weights, easily interpretable. The collective weights are to be obtained by averaging all weights by different initial conditions and data subsets, where we need to stabilize the learning processes and the corresponding connection weights. For this purpose, we introduce the connective potential information reduction. The potential information is related to the concept of entropy, but it has a more concrete meaning of how many connection weights are connected with the corresponding neurons. When the potentiality is higher, the number of connection weights between neurons becomes larger. For improved interpretation, we need to reduce the potential information, because it is much easier to understand the inference mechanism with a smaller number of connection weights, though too much reduction may degrade generalization performance. For compromising between interpretation and generalization, we introduce the variance of potentiality, and we try to solve the contradiction of improved interpretation and generalization. We applied the method to the diabetic data set, where we first tried to show that the potential information could be reduced and at the same time the variance of potential information could be changed considerably in intermediate layers. Finally, those compressed weights over different initial conditions and different patterns were averaged to get the final collective weights for interpretation, which turned out to be ones showing easily interpretable, linear, and independent relations between inputs and targets.
Methodological Support of the Work of Primary Schoolchildren with Information Presented in the Form of Diagrams

The article outlines a range of methodological problems that arise when teaching younger schoolchildren, the ability to use information presented&#x0D; in the form of diagrams. Modern textbooks of mathematics for elementary school and methodological recommendations to them related to the&#x0D; topic “Diagrams” of the section “Working with information” are analyzed. Since the development in younger schoolchildren of the skills of&#x0D; working with visual sign-symbolic means of graphical presentation of information is one of the requirements of the Federal State Educational&#x0D; Standard of Primary General Education for meta-subject learning outcomes, the author proposes methodological techniques and a system of&#x0D; assignments that allow to eliminate the current and potential difficulties of primary school students when mastering charts.
VCC-BPS: Vertical Collaborative Clustering using Bit Plane Slicing

The vertical collaborative clustering aims to unravel the hidden structure of data (similarity) among different sites, which will help data owners to make a smart decision without sharing actual data. For example, various hospitals located in different regions want to investigate the structure of common disease among people of different populations to identify latent causes without sharing actual data with other hospitals. Similarly, a chain of regional educational institutions wants to evaluate their students’ performance belonging to different regions based on common latent constructs. The available methods used for finding hidden structures are complicated and biased to perform collaboration in measuring similarity among multiple sites. This study proposes vertical collaborative clustering using a bit plane slicing approach (VCC-BPS), which is simple and unique with improved accuracy, manages collaboration among various data sites. The VCC-BPS transforms data from input space to code space, capturing maximum similarity locally and collaboratively at a particular bit plane. The findings of this study highlight the significance of those particular bits which fit the model in correctly classifying class labels locally and collaboratively. Thenceforth, the data owner appraises local and collaborative results to reach a better decision. The VCC-BPS is validated by Geyser, Skin and Iris datasets and its results are compared with the composite dataset. It is found that the VCC-BPS outperforms existing solutions with improved accuracy in term of purity and Davies-Boulding index to manage collaboration among different data sites. It also performs data compression by representing a large number of observations with a small number of data symbols.
An entity relation extraction algorithm based on BERT(wwm-ext)-BiGRU-Attention

Entity relation extraction is one of the basic steps of knowledge Graph. It identifies the relations between entities. A BERT-Bidirectional gated recurrent units-Attention mechanism (BERT-BiGRU-Attention) model has been proposed, but it is based on the single Chinese character based masking. Due to the complexity of Chinese grammar structure and the semantic diversity, a BERT(wwm-ext) was proposed based on the whole Chinese word masking. In this paper we propose a BERT(wwm-ext)-BiGRU-Attention model. The experimental result shows that for the purpose of entity relation extraction the precision is 93.60%, recall rate is 91.90%, and F1 value 92.53%, which are higher than the BERT-BiGRU-Attention and its precision is 91.80%, recall rate is 90.16%, and F1 value is 90.97%. Since BERT(wwm-ext)-BIGRU-Attention gets higher precision, F1 value, and higher recall rate, it has better effects on the Chinese entity relation extraction tasks.
Query-Based Recommendation by HIN Embedding with PRE-LSTM

In recent years, as more and more big data platforms have been applied to the government network systems, it’s essential to adopt an effective query-based recommendation algorithm to help officers find out the needed table with a keyword. The key challenge lies in the complex relationship among data from different departments, which cannot be easily solved by the existing database query methods. The Heterogeneous Information Network (HIN) is a specific type of networks developed for modeling complex data relations. However, these existing query-based recommendation algorithms could not make use of HIN. Besides, many query-based recommendation algorithms could not make recommendations with the keyword that is not in the query records. In addition, most of the existing recommendation algorithms do not make full use of the semantic meanings. Therefore, in this paper, by making use of the real dataset provided by the local government, the proposed method is the first to use the pretrained word embeddings and LSTM (PRE-LSTM) to train the network embeddings and to learn the relationship among the tables, departments and the keywords, so that it can make use of the data in HIN and enable the network embeddings to obtain the most precise semantic meaning. Additionally, our algorithm uses word embeddings to represent query keywords so as to let our algorithm make the query-based recommendation for nearly any query. Using the trained embeddings and the PRE-LSTM model, the proposed algorithm is able to show user-specific recommendation results sorted in a reasonable order. Experimental results on the real data application tasks confirm the effectiveness of the proposed method.
A Novel Approach for CBIR Using Four-Layered Learning

Content-based image retrieval (CBIR) comprises recovering the most outwardly comparative images to a given question image from a database of images. CBIR from therapeutic image databases does not plan to supplant the doctor by anticipating the sickness of a specific case however to help him/her in analysis. The visual attributes of an ailment convey analytic data, and periodically outwardly comparative images relate to a similar infection class. By counseling the yield of a CBIR framework, the doctor can acquire trust in his/her choice or considerably think about different potential outcomes. With high-dimensional information in which every point of view on information is of high spatiality, determination of highlights is imperative to further build the aftereffects of bunching and characterization. To ease the enthusiastic miscellany in the precision of image retrieval, we developed another graph-based learning strategy technique to successfully recover images from remote detecting. The proposed strategy utilizes a four-layered framework that joins the feature level fusion of Gabor and ripplet Transform of selected query along with SVR. In the first layer two image sets are retrieved utilizing the Gabor and Ripplet-based wavlet Decomposition separately, and the besides, the top ranked retrieved images from both the top up are further used to find their queries. Using each individual part, the chart grapples recoup six image sets from the image database as an augmentation request in the subsequent layer. The photos in the six image sets are evaluated for positive and negative data age in the third layer, and Simple MKL is associated with gain proficiency with the proper inquiry subordinate combination loads to accomplish the last consequence of image recuperation. This research is based on building fully-automatic four layer systems capable of performing large-scale image search based on texture information. An effective four layer architecture with the application of SVR was proposed in this study for the purpose of retrieving images from CIFAR dataset.
Web-based geographic information system for flood disaster using spatial autoregressive method

Abstract The territory of Indonesia is classified as one of the countries prone to disasters, both natural disasters and disasters caused by human activities. Flood disaster is one example of a disaster that often occurs, especially in the DKI Jakarta area. The common cause of the flooding is usually caused by high rainfall exceeding the average in general so that water sources such as rivers, reservoirs, and lakes cannot accommodate the amount of rainwater. This is also supported because the water absorption system is minimal so that it is unable to absorb excess water, causing flooding. The method used to define the relationship between various factors and the relationship between regions is to use the Spatial Autoregressive (SAR) method. In order to get a better relationship between regions, the existing spatial data will be visualized with the Geographical Information System. This research focuses on creating a web-based Geographic Information System application. Applications created using the programming language PHP and Javascript with the Codeigniter framework. The results obtained by the SAR equation to predict the height of flooding in the DKI Jakarta Province and the test results using the Mean Absolute Deviation (MAD) were 1.354656% while using the Mean Absolute Percentage Error (MAPE) was 2.157288%. Testing applications using the black box testing method was obtained by 93.35%. This shows that the SAR Model is suitable for predicting flood heights in the DKI Jakarta Province. This flood prediction can be used one year from the prediction model made.
Speculo: Pattern Recognition with a Deep Convolutional Inverse Graphics Network for Face-Indexing

Footage analysis procedures require manual examination which can be time consuming. As an alternative to the above procedure, we propose a system capable of automating footage analysis by fingerprinting all identified faces and classifying them into unique faces, regardless of the angle the face was found in the footage. This system also has the ability to expand the number of faces it can recognize during runtime opposed to detecting a fixed number of faces.
창조성 신장을 위한 자유학기제 운영 방안

The student-centered classes in a free semester system have the power to grow both students and teachers.By taking the initiative in taking part in the class, the field of class is expanded, the interest in the class is increased, and the students have an active attitude to discover and solve problems. In addition, by cooperating and collaborating with friends in the course of problem solving, the ability to develop community consciousness, creative problem solving power, sociality and personality, and actively design and explore career paths while encountering various career exploration opportunities is cultivated.On the part of teachers, they also lead various class research and project activities, improving their ability and expertise, and giving them the ability to conduct free semester classes in various forms.In other words, both students and teachers are given opportunities to grow and grow each other''s capabilities through improved classes.Knowing the fun of participating classes and studying extensively will improve the excessive academic burden and performance-oriented climate given to students, and restore trust in school education. However, it is necessary to control the nature of the classes so that they are not too focused on play and activities, and to spread the free semester system, the school needs to organize curricula, adjust the number of hours for each subject, and support for the school''s autonomous curriculum operation.