Reviewerly/exHarmony huggingface dataset:
Non-Parametric Subject Prediction

Automatic subject prediction is a desirable feature for modern digital library systems, as manual indexing can no longer cope with the rapid growth of digital collections. This is an “extreme multi-label classification” problem, where the objective is to assign a small subset of the most relevant subjects from an extremely large label set. Data sparsity and model scalability are the major challenges we need to address to solve it automatically. In this paper, we describe an efficient and effective embedding method that embeds terms, subjects and documents into the same semantic space, where similarity can be computed easily. We then propose a novel Non-Parametric Subject Prediction (NPSP) method and show how effectively it predicts even very specialised subjects, which are associated with few documents in the training set and are not predicted by state-of-the-art classifiers.
Deep Learning in the Field of Art

The results of deep learning in the art world have changed people's traditional perceptions, more and more people believe that it's not a long way to go when AI finally replace artists. This paper reviews the application of deep learning in painting, music, literature, and introduces the classical models and algorithms in those art fields.
Extracting heavy metal stress indicators from remote sensing imagery using WOFOST model and wavelet packet decomposition algorithm

Abstract Heavy metal pollution of crops seriously endangers food security and indirectly threatens human health. Direct measures in the fields and laboratories through on-site sample collection, testing, and analysis are time-consuming and labor intensive, thereby prohibiting their applications in large-scale monitoring. Remote sensing techniques provide an alternative means through examining above-ground vegetation status, e.g. leaf area index (LAI). Heavy metals, however, are typically accumulated in the root of crops, which may also be affected by a large number of external environmental factors besides heavy metals. The objective of this paper, therefore, is to identify heavy metal stress indicators of crops through integrating LAI extraction from remote sensing imagery, weight of rice roots (WRT) estimation by the World Food Study (WOFOST) model, and heavy metal stress indicator identification with the wavelet packet decomposition (WPD) method. First, LAI was retrieved from the HJ CCD data over three continuous years through constructing a relationship between LAI and normalized difference vegetation index (NDVI). Next, dry weight of rice roots (WRT) curves over these three continuous years were estimated using the WOFOST model with multi-temporal LAIs are inputs. Finally, a component (e.g. cfs 14) was identified to represent the heavy metal pollution status with the Wavelet Packet Decomposition (WPD) of the WRT curves of these three years. Validation results suggest that the identified component can successfully represent different levels of heavy metal stress.
An Online Pattern Based Activity Discovery: In Context of Geriatric Care

The behavioral analysis of an elderly person living independently is one of the major components of geriatric care. The day-long activity monitoring is a pre-requisite of the said analysis. Activity monitoring could be done remotely through the analysis of the sensory data where the sensors are placed in strategic locations within the residence. Most of the existing works use supervised learning. But it becomes infeasible to prepare the training dataset through repeated execution of a set of activities for a geriatric person. Moreover, the geriatric people are annoyed to use wearable sensors. Thus it becomes a challenge to discover the activities based on only ambient sensors using unsupervised learning. Pattern-based activity discovery is a well-known technique in this domain. Most of the existing pattern based methods are offline as the entire data set needs to be mined to find out the existing patterns. Each identified pattern could be an activity. There are a few online alternatives but those are highly dependent on prior domain knowledge. In this paper, the intention is to offer an online pattern based activity discovery that performs satisfactorily without any prior domain knowledge. The exhaustive experiment has been done on benchmark data sets ARUBA, KYOTO, TULUM and the performance metrics ensure the strength of the proposed technique.
Requirement-Based Test Approach and Traceability for High-Integrity Airborne Embedded Systems

One of the biggest challenges in the development of airborne embedded systems is to ensure that the aircraft subsystem meets all its user specifications and ascertain that no important functionality is missing which leads to development of an incorrect product. This chapter proposes a methodology for achieving requirement traceability and thereby performing requirement-based testing for efficient test and evaluation of aircraft subsystems. This methodology integrates requirement traceability throughout the software development life cycle along with requirement-based testing for high-integrity software systems. The methodology has been found to be most effective in revealing errors and optimizes testing by preventing repetition of test cases across test platforms. This unique contribution has the potential to revolutionize the research world in software engineering.
Use and application of information and communication technologies in the students of the degree in administration of the Tecnologico de Calkini

Currently, ICTs are necessary for growth and educational and social development. However, the simple provision of infrastructure and technological equipment to schools is insufficient, These require its proper articulation in the teaching-learning processes and proper school management. for this reason it is imperative to evaluate the use of ICT in young students of higher level in the Maya area of the Camino Real of the State of Campeche. The study is descriptive, with a quantitative approach, the design is non-experimental transactional, the method was the field study and the technique is the survey with the questionnaire as an instrument. The results indicate that one of the most deeply rooted devices among students is the cell phone; the Laptop is the machine that most students use to do their jobs and tasks; for this reason, this device represents an opportunity to work more with them, this device represents an opportunity to work more with them, this is favored by the public policies that have taken place in the area and have allowed students to access the internet in schools. Therefore, an ICT policy in the institution favors young students to increase their potential in the use of these technologies.
Predicting Candidates For Fit And Proper Test Using K-Nearest Neighbor

Existing condition for Fit and Proper Test using filtering method based on criteria, result of this method are many filtered candidates hence needed times to doing the test. In other side, allocation time should suitable by maturity level of both candidates because examiner is a basic management till high management. The method to measure predicting candidate is not exist, then this research using K-Nearest Neighbor. K-Nearest Neighbor is the method able to predicting based on data that measuring the closest distance with successful sample or unsuccessful sample that have a position. To measure the distance, the method using criteria such as grade, assessment, competency certificates, talent score, education, last position term, alignment with new position. KNN predicting 5 employee who have True Positive (actual is success then prediction is Success), it can reduce candidate more 40 percent. From Common Measurement explained that K=1 more accurate and precision than K=3 and K=5 but compared with True Positive and False Positive K=5 have best than K=1 and K=3.
Role-Based Static Desensitization Protection Method for Privacy Data

One of the major and serious threats on the Internet today is privacy disclosure. This paper studies the role-based static desensitization method of sensitive data. The method can set different fields and different desensitization methods for different roles to meets different protection requirements. The application submits the database operation request to the server. Then the server will desensitize the operation field value in the request according to the type of the request (INSERT, UPDATE, SELECT) and user's role, and submit the desensitized request to database. Since the database stores the desensitized data, and multiple desensitization functions and desensitization parameters are used in the desensitization process, the malicious destroyer cannot obtain the raw data even if he can access the database.
Study on Evaluation Method of Teaching Quality Based on Cloud Model and D-S Theory

The Evaluation of teaching quality is a complicated project. It’s a typical problem of uncertainty. A design scheme of teaching quality evaluation method based on cloud model and D-S theory is proposed with the school teaching evaluation index. With the experiences and knowledge of experts, evaluation index was described by cloud model and judgment matrix of AHP was revised by D-S theory. The weight of evaluation indexes was determined reasonably and objectively. Applied in the actual environment, the method can make an accurate and efficient evaluation with a certain practical value.
Discrete optimizations using graph convolutional networks

In this paper we discuss the use of graph deep learning in solving quadratic assignment problems (QAP). The quadratic assignment problem is an NP hard optimization problem. We shall analyze an approach using Graph Convolutional Networks (GCN). We prove that a specially designed GCN produces the optimal solution for a broad class of assignment problems. By appropriate training, the class of problems correctly solved is thus enlarged. Numerical examples compare this method with other simpler methods.
Methods of forming the join table

Abstract The present work considers the methods of forming data views as a join table intended to be used in table-driven user applications, such as spreadsheets, multidimensional arrays, etc. The main purpose of the join table is to organize the interaction of such applications with the corporate database. The join table can be used at a physical level. This will significantly reduce the time required to generate custom data views in the applications. The use of the join table becomes inefficient, if it needs to be changed in a short period of time. However, the use of storage devices with associative access or “logic-per-track” devices for data processing will significantly reduce the join table transformation time.
Standardization and standards in Bulgarian libraries: Current state

The article contains the results of an empirical study conducted in the early 2018 through a survey conducted among the libraries in Bulgaria. Data obtained were processed by the software product for mathematical analysis of statistical data SPSS for Windows 19.0. The aim of the study is to create a complete picture of the Bulgarian library system in terms of understanding the benefits of standardization activity and the application of specific standards in the field of library activities. The applied research methods are: research, analysis and synthesis of information received, a comparative analysis between different groups of libraries participated in the survey. In order to specify the psychometric characteristics of the methods and verification of the working hypothesis, the following methods for statistical processing were applied: descriptive statistics; correlation analysis; factorial analysis; one-way ANOVA test. This study is the first large-scale study of its kind on standardization among Bulgarian libraries. Its contributions can be assigned to those that enrich the theory and methodology of sociological studies in the field of library activities. As a result of the survey the current level of application of the specific national, branch and international standards in the Bulgarian libraries was established. Measures have been identified to promote the standardization activity in the field of library activities. Data gathered from the survey contribute to the development of methodology and curricula for online training of the library specialists, according to their specific needs and interest.
Comparison Between U-Net and U-ReNet Models in OCR Tasks

The goal of this paper is to explore the benefits of using RNNs instead of using CNNs for image transformation tasks. We are interested in two models for image transformation: U-Net (based on CNNs) and U-ReNet (partially based on CNNs and RNNs). In this work, we propose a novel U-ReNet which is almost entirely RNN based. We compare U-Net, U-ReNet (partially RNN), and our U-ReNet (almost entirely RNN based) in two datasets based on MNIST. The task is to transform text lines of overlapping digits to text lines of separated digits. Our model reaches the best performance in one dataset and comparable results in the other dataset. Additionally, the proposed U-ReNet with RNN upsampling has fewer parameters than U-Net and is more robust to translation transformation.
Design and implementation of military chess game algorithm based on probability model and situation evaluation

Military chess is a very popular board game in China and a branch of the imperfect information game. This paper starts from the characteristics of imperfect information game and military chess game. Focus on the probability model, the evaluation of the method of travel, and the search of chess in the game of military chess, designed a military chess game system based on probability model and situation assessment, improved the intelligence and strategic of the system.
Developing causal interpretations for high and low level light used in quantum remote sensing

We present observational and causality arguments to underscore that a better model for light is a “hybrid photon” wave packet. At the moment of quantum transitions, the electromagnetic energy is embedded in the transient quantum, hν. But, it immediately evolves into a diffractively spreading classical, quasi-exponential, EM wave packet. This hybrid photon accommodates both quantum and classical optics. The quantum formalism has demonstrated staggering successes in modeling the micro world of atoms. The Huygens-Fresnel diffraction integral and Maxwell’s wave equation are also enjoying continued successes since early 1800’s. In this paper, using the model of hybrid photon, we underscore that the photoelectron counting statistics should vary depending upon the relative phases, spacing and amplitudes of the superposed wave packets (hybrid photons) as they simultaneously arrive and stimulate the quantum mechanical dipole complexes on the surface of the photo detectors.
Application of a full-dimensional observable smoothing algorithm in SINS

The traditional zero-velocity correction algorithm (ZUPT) can theoretically suppress the accumulation of navigation errors, but it can only correct the state errors of the zero-velocity interval and the observations are less. The state information of the model solution based on the SINS algorithm and the navigation error equation in the non-zero-velocity interval causes a sudden change in the entire pedestrian trajectory process, that is, the stability is poor and the accuracy is not high. This paper proposed a algorithm, which 3D errors of attitude and position are added as observations to achieve full-dimensional observability based on the traditional zero-velocity correction, then all state errors are obtained by EKF estimation, and a post-processing smoothing algorithm is introduced to make full use of the measurement information over the entire time period to correct the navigation errors of the non-zero-velocity interval. In order to verify the accuracy and stability of the algorithm, experiments were carried out by using the self-developed IMU. The results show that the proposed algorithm has better stability than the traditional ZUPT, improves the smoothness of the trajectory, and the navigation accuracy is improved by 1.7%.
Research on Game Problem Under Incomplete Information Condition Based on Deep Reinforcement Learning

Deep reinforcement learning has been widely used in the military field and applied to many fields, especially in the areas of identification, recommendation, decision-making and so on. This paper analyzes the prediction error of game confrontation by deep reinforcement learning under the condition of incomplete information, and proves the effectiveness of deep reinforcement learning for machine game.
Study on Digital Exchange Platform of University Information Resources

Digital exchange platform is a comprehensive service platform. In this paper, the construction scheme of the platform is proposed. By constructing the data exchange platform, a people-oriented information environment was built to meet demands of faculty, staff and students on information resources. And thus a digital campus was constructed to provide efficient online services and information resources. The experimental results show that the platform is user-friendly, runs orderly, integrates the service, and provides a good reference value for the data integration of enterprises and universities in the future.
Use of Drones with Virtual Reality Application for Immobile Patients An Analysis of the Impact on Life Quality

Progressive digitalisation makes a consistent integration of new technologies in the medical field inevitable in the long term. Especially the application of virtual reality and drones offers great potential for further developments in this sector but is still largely unexplored. Due to this fact, in the present trial it was investigated whether or to what extent the use of VR-drone-couplings can influence the quality of life of immobile patients. In order to analyse this impact, a field trial was carried out with ten persons who were not handicapped. Their quality of life was assessed by using the questionnaire WHOQOL-BREF before and after the experiment and the values were compared subsequently. By incorporating a systematic literature search, the trial results could be applied to the use of drones by immobile patients. The results show that the VR-drone-deployment can positively influence the quality of life of both healthy and movement-restricted individuals, but only to the extent that the resulting mobility increase is weighted in the life of those affected. The use of a video drone enables locally fixed persons to expand their mobility radius, as well as to overcome barriers and maintain social contacts. Accordingly, it is important to pursue this approach and to carry out additional studies regarding this concern in the future. A detailed breakdown of the results, as well as their consequences for the use of drones by paraplegic patients in the future is represented in this paper. Moreover, the need of further adaptions, changes and research which was revealed by this investigation can be taken from the following study.
Topology Acquisition in Unknown Environment and Learn the Route to Destination Point by Autonomous Mobile Robots

It is an important task for mobile robots that search the target and learn the route while recognizing the unknown environment topology. Usually, reinforcement learning is used as a learning method to know the route to the target while exploring the environment. However, in an unknown environment, it is difficult to predict the number of state division. Particularly, when the state division is too fine, the amount of calculation increases exponentially. In this paper, we propose a method to dynamically construct the state space of the environment using Growing Neural Gas and simultaneously search and learn the route to the target using Q-Learning. We applied multiple autonomous mobile robots to increase searching efficiency. The experimental result shows the effectiveness of the proposed method that can respond to dynamic environmental change.
An Investigation on Multi View Based User Behavior Towards Spam Detection in Social Networks

Online Social Networks have become immensely vulnerable for spammers where they spread malicious contents and links. Understanding the behaviors across multiple features are essential for successful spam detection. Majority of the existing methods rely on single view of information for spam detection where diversified spam behaviors may not allow these techniques to be survived. As a result, Multiview solutions are getting emerged. Based on homophily theory, a hypothesis of spammer’s behaviors should be inconsistent across multiple views compare to legitimate user behaviors is defined. We investigated the consistency of the user’s content interest and popularity over multiple topics across multiple views. The results confirm the existence of notable difference of average similarity between legitimate and spam users. It proved that the legitimate user behaviors are consistent across multiple views while spammers are inconsistent. This indicates that consistency of user behavior across multiple views can be used for spam detection.
Comparison Between Filter Criteria for Feature Selection in Regression

High-dimensional data are ubiquitous in regression. To obtain a better understanding of the data or to ease the learning process, reducing the data to a subset of the most relevant features is important. Among the different methods of feature selection, filter methods are popular because they are independent from the model, which makes them fast and computationally simpler than other feature selection methods. The key factor of a filter method is the filter criterion. This paper focuses on which properties make a good filter criterion, in order to be able to select one from the numerous existing ones. Six properties are discussed, and three filter criteria are compared with respect to the aforementioned properties.
PENERAPAN 5R (RINGKAS, RAPI, RESIK, RAWAT, RAJIN) TERHADAP HASIL JAHITAN BUSANA WANITA SISWA SEKOLAH MENENGAH KEJURUAN

The purpose of this study was to determine the effect of applying 5R (Concise, Neat, Clean, Care, Diligent) on the results of women's fashion stitches. This research is an ex-post facto type. The population amounted to 117, the sampling technique using the Issac and Michael formula at a significant level of 5% obtained a sample of 90 students. The method of data collection uses the questionnaire and documentation method. The data analysis technique is descriptive and hypothesis testing uses the Product Moment correlation test. The results of the study showed that there was a positive and significant effect of the application of 5R on the results of the stitches with a contribution of 31.1%. The 5R when learning sewing practices is mostly classified as sufficient with a relative frequency of 74.44%. Most stitches are in the good category with a relative frequency of 62.22%.
COMPLEXITY OF SPECIALIZED FORMS OF CULTURE AS A FACTOR FOR CULTURAL COMPLICATION

The article considers the problem of complexity in culture through the lenses of its specialized forms, identifies the patterns of culture complication. Progress in art is interpreted as one of the aspects of humanitarian progress. On the one hand, art objects are invaluable and unique; on the other hand, specialized forms of culture represent cultural complexity. Specialized forms of culture can be considered as a cause and consequence of cultural complication. The following criteria of art complication are identified: perfect performance technique, originality, meaningful fullness, “fractal narration”, subjects’ ability for aesthetic reflection, etc. The author concludes that when it comes to complexity of specialized forms of culture, complexity should be considered as an anthropo-cultural phenomenon.