Reviewerly/exHarmony huggingface dataset:
Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack

Fast and accurate feature extraction can lay a solid foundation for scene reconstruction and visual odometry. However, this has been rather a difficult problem for the focused plenoptic camera. In this paper, to the best of our knowledge, we first introduce an accurate and fast feature extraction algorithm based on central projection stereo focal stack (CPSFS). Specifically, we propose a refocusing algorithm that conforms to the central projection with regard to the center of main lens, which is more accurate than traditional one. On this basis, the feature is extracted on the CPSFS without calculating dense depth maps and total focus images. We verify the precision and efficiency of the proposed algorithm through simulated and real experiments, and give an example of scene reconstruction based on the proposed method. The experimental results show that our feature extraction algorithm is able to support the feature-based scene reconstruction via focused plenoptic camera.
A Multispectral Light Field Dataset for Light Field Deep Learning

Deep learning undoubtedly has had a huge impact on the computer vision community in recent years. In light field imaging, machine learning-based applications have significantly outperformed their conventional counterparts. Furthermore, multi- and hyperspectral light fields have shown promising results in light field-related applications such as disparity or shape estimation. Yet, a multispectral light field data\-set, enabling data-driven approaches, is missing. Therefore, we propose a new synthetic multispectral light field dataset with depth and disparity ground truth. The dataset consists of a training, validation and test dataset, containing light fields of randomly generated scenes, as well as a challenge dataset rendered from hand-crafted scenes enabling detailed performance assessment.
Enhancement Performance of Multiple Objects Detection and Tracking for Real-time and Online Applications

Multi-object detection and tracking systems represent one of the basic and important tasks of surveillance and video traffic systems. Recently. The proposed tracking algorithms focused on the detection mechanism. It showed significant improvement in performance in the field of computer vision. Though. It faced many challenges and problems, such as many blockages and segmentation of paths, in addition to the increasing number of identification keys and false-positive paths. In this work, an algorithm was proposed that integrates information on appearance and visibility features to improve the tracker's performance. It enables us to track multiple objects throughout the video and for a longer period of clogging and buffer a number of ID switches. An effective and accurate data set, tools, and metrics were also used to measure the efficiency of the proposed algorithm. The experimental results show the great improvement in the performance of the tracker, with high accuracy of more than 65%, which achieves competitive performance with the existing algorithms.
Professional Self-Consciousness As Factor Of Professional Formation And Self-Development Of Personality

The article reveals theoretical approaches to the study of the formation of the professional identity of university students.The relevance of the task of forming the professional identity of university students is revealed.Professional self-awareness is considered a complexly organized, sustainable education, showing certain features in the process of professional development regarding its structure and formation.The role of professional self-awareness as a factor of the personality development and professional development in general and in conjunction with the effectiveness of this activity is analyzed.The process of the formation of professional self-awareness is considered as qualitative and quantitative changes in the content of cognitive, emotional, and behavioral components.The structure of professional selfawareness is presented: a three-component structure of professional self-awareness is highlighted, which includes cognitive, affective, and behavioral components.It is shown that professional self-awareness is a dynamic formation; it can develop, change, expand.It is revealed that, as the development of the educational and professional activity of the subject becomes complicated, its relationship with the subject and social world is improved, due to the specifics of the profession.This circumstance is a prerequisite for the further development of the subject's professional self-awareness, in favor of its increasing adequacy and sufficient differentiation.Professional self-awareness is the factor that regulates the implementation by the subject of current activity as a condition for its development, as well as the professional development of the subject as a whole.
A MULTI-CLASSIFIER APPROACH FOR TWITTER SPAM DETECTION USING INNOVATIVE ANN-FDT ALGORITHM

Nowadays, various social media platforms are available in Internet like Facebook, Twitter and Instagram for uniting the people.Twitter is one among the most famous platform in social media due to its available information among users.Users allows to find new friends and update their latest information and activities.Twitter is using Google Safe-browsing to detect the spam URL and block spam links.Due to the presence of advanced API which enables to read and write the data in Twitter, different kinds of spammers are attracted in the Twitter.There are various existing researches applied various machine learning techniques to determine the twitter spam.However, there is no comprehensive evaluation on their algorithms and lack of accuracy in large dataset.To rectify these issues, this research proposed hybrid method with the combination of Artificial Neural Networks with Fuzzy Decision Tree (ANN-FDT).The proposed classifier classified the span and non-span tweets based on the labels.For experimental analysis, the proposed classifier applied on large dataset of 600 million public tweets.The performance of proposed algorithm is evaluated by means of measures like accuracy, TPR, FPR and F-measure.From the results it can be seen that the proposed technique has improved performance.
Genetic Algorithm to Estimate Parameters of Indonesian Population Growth Model

In this study, the genetic algorithm is implemented to determine the most suitable growth models for Indonesian population data.The tested models are the simple models of Malthus and Verhulst.Parameters estimated in Malthus model include birth rate, death rate, and migration rate.Meanwhile, Parameters estimated in Verhulst model are intrinsic growth rate (birth rate minus death rate), carrying capacity, and migration rate.The model selection is based on the lowest average cost function value of each model.The value of the cost function is determined based on the distance between the population number in the model with the estimated parameters and the population number reported by worldbank.org.After determining the most appropriate model based on parameter estimation, simulation of the Indonesian population will be conducted for the upcoming years.
Blackbox Testing Using Fuzzy Clustering Based on Boundary Value Analysis on The Text Opinion Mining Application in Traditional Culture Arts Presentation

The success of organizing a traditional work of art cannot be separated from the important role of data and information obtained from the public in general, and viewers or art connoisseurs in particular. This information is an indicator that can be used to measure the amount of public attention to traditional arts, which is an effort to promote traditional cultural arts. Data and information related to traditional artworks were obtained from filling out the instruments that were distributed to the public online to produce an opinion form that contained a complete description with a discussion containing the aesthetic of the artwork. Opinion data is needed as a measure of progress and preservation of a work of art. The linguistic measurement of opinion can be solved using fuzzy methods in a cryptic form that can be weighted. In this study, the authors tested the audience opinion text mining application on the presentation of traditional cultural artworks using fuzzy clustering using the functional testing method (Black box testing). Through this test will be discussed related to the menu or module to produce information.
Higher School Education Trends In Russia And Russian North

Education is the leading form of human activity.A high level of education ensures putting knowledge into practice through the advanced information resources use.Education is a single dedicated process of upbringing and training carried out in the interests of a man, family, society and the state.The formation of educational policy at all levels indicates that the higher education sphere plays a significant role in international educational space and socio-economic communities of all countries.Accordingly, the authors investigated the top educational trends based on the scientific literature study and materials systematization.The results were presented to the international scientific and pedagogical community.The work aimed to conduct the theoretical analysis of higher education system modernization processes in Russia and in the North of Russia.To study the problem of higher education quality was carried out the survey of the pedagogical universities students' attitudes to learning.Students are a mobile social group, its composition, values and reasons for obtaining an education are constantly changing.Thus, in order to ensure the education quality, it is necessary to reveal the students' attitude to learning regularly.As the results, a scientific literature theoretical analysis of the finding out the trends of higher education system development and the study of students' attitudes to it by means of questionnaires was conducted.Received data can contribute the quality of higher education system.
The Cult Of Book In Regulating Self-Perception

The article studies the social phenomenon "the cult of book" that continues to be relevant and can influence self-perception.Based on the basic concepts "human factor", "personal self-perception", the authors consider the book as a valid tool for their development.A full-fledged person is always interested in his physical state and self-understanding (national, genetic, collegial, etc.).This combination serves as a stimulus for the existence of a multilaterally developed personality.This communication in the readership and publishing perspective can serve as a confident motive for creativity.Publication of a book requires a thorough approach.Only if the spirit of work created from various sources will be saturated, circumstances can change.The facts of the history of the cult, circumstances of prohibitions their conditionality are presented.When addressing the issues of book publishing, considering a whole range of archival documents, the article analyzes a number of actions, describes the current situation and concludes about the efficiency of the cult.The development of a constant interest in reading is a family mechanism; the goal of book producers is to encourage any socially-conditioned reader to make mental attempts.
DIAGNOSA HAMA DAN PENYAKIT PADA TANAMAN PADI MENGGUNAKAN METODE NAIVE BAYES DAN K-NEAREST NEIGHBOR

This study builds systems Case Based Reasoning (CBR) to diagnose pests and diseases in rice plants using Naïve Bayes algorithm and K-Nearest Neighbor. CBR is one method of solving the problem with new cases of decision making based on the solution of previous cases by calculating the degree of similarity (similarity), The case consists of 13 species and 10 types of disease pests of rice plants. The degree of similarity can be determined by indexing and nonindexing. Indexing is the process of grouping the cases by classes that have been determined, while nonindexing a process without grouping cases. Based on cross validation testing using average values obtained accuracy of 92.88% to 153 test data on testing using the indexing and the average value of 89.63% accuracy of the test data in the test 153 using nonindexing.
The Implementation of Soft System Methodology (SSM) for Systems Development in Organizations (Study Case: The Development of Tourism Information System in Palembang City)

Nowadays the massive improvement in Information T echnology, which integrates computer technology and telecommunication, has lead to revolution in the field of Information System.T he positive momentum in the world of information technology that is indicated by the rapid growth of Internet usage should become a positive enablement in the development of tourism industry.T he implementation of information technology could help the tourist in finding the information about tourist attractions, its facilities, ro ute, distance, cost required to get there.Soft Systems Management (SSM) is one of the best options that can be used to support and define the tourism information system (e -tourism).T he SSM defined system is expected to meet the system stakeholder requirements, such that give optimum value to the tourist and help the country to attract more tourists visiting the Indonesia.
Using Linked Data to Help Robots Understand Product-related Actions.

Household robots need semantics to understand that a detergent is a cleaning product that can be used to clean physical objects like a table, but laundry detergent is only used to clean/wash laundry. A safely acting autonomous robot should also know that both will not be used as ingredients for meal preparation. We propose a new approach to connect robot sensor data to Linked Data in order to give robotic agents semantic product information about objects that can be found in their environment so that the action to be performed with a given object can be inferred. For this, we use the robot’s belief state when recognizing a product and link it to a product ontology that follows Semantic Web standards. We then use the product class information to fetch further information from external sources like Wikidata or ConceptNet that contain action information (e.g. laundry detergent is used for laundering). At last, the action results are mapped to internally known actions of the robotic agent so that it knows which action can be performed with the perceived object.
Perbandingan Performansi Kinerja Algoritma Pengklasifikasian Terpandu Untuk Kasus Penyakit Kardiovaskular

One of the health problems that occur in Indonesia is the increasing number of NCD (Non-Communicable Disease) such as heart attack and cardiovascular disease. There are two factors that cause cardiovascular disease, i.e. factor that can be changed and cannot be changed. This study aim to analyze the best performance of several classification algorithms such as k-nearest neighbors algorithm (k-NN), stochastic gradient descent (SGD), random forest (RF), neural network (NN) and logistic regression (LR) in classifying cardiovascular based on factors that caused those diseases. There are two aspects that need to be examined, the performance of each algorithm which is evaluated using the Confusion matrix method with the parameters of accuracy, precision, recall and AUC (Area Under the Curve). The dataset uses 425.195 samples from result data of cardiovascular disease diagnosed. The testing mode uses percentage split and cross-validation technique. The experimental results show that the performance of NN algorithms produces the best prediction accuracy compared to other algorithms, which is accuracy of 89.60%, AUC of 0.873, precision of 0.877, and recall of 0.896 using percentage split and cross-validation testing mode using Orange. For the accuracy of 89.46%, AUC of 0.865, precision of 0.875, and recall of 0.895 using cross-validation testing mode using Weka. By KNIME, the result of accuracy value is 88.55%, AUC value is 0.768, precision value is 0.854, and recall value is 0.886 using cross-validation testing mode.
Joint Geosequential Preference and Distance Metric Factorization for Point-of-Interest Recommendation

Point-of-interest (POI) recommendation is a valuable service to help users discover attractive locations in location-based social networks (LBSNs). It focuses on capturing users’ movement patterns and location preferences by using massive historical check-in data. In the past decade, matrix factorization has become a mature and widely used technology in POI recommendation. However, the inner product of latent vectors adopted in matrix factorization methods does not satisfy the triangle inequality property, which may limit the expressiveness and lead to suboptimal solutions. Besides, the extreme sparsity of check-in data makes it challenging to capture users’ movement preferences accurately. In this paper, we propose a joint geosequential preference and distance metric factorization framework, called GeoSeDMF, for POI recommendation. First, we introduce a distance metric factorization method that is capable of learning users’ personalized preferences from a position and distance perspective in the metric space. Specifically, we convert the user-POI interaction matrix into a distance matrix and factorize it into user and POI dense embeddings. Additionally, we measure users’ personalized preference for the POI by using the Euclidean distance metric instead of the inner product. Then, we model the users’ geospatial preference by applying a geographic weight coefficient and model the users’ sequential preference by using the Euclidean distance of continuous check-in locations. Moreover, a pointwise loss strategy and AdaGrad algorithm are adopted to optimize the positions and relationships of users and POIs in a metric space. Finally, experimental results on three large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method.
METHODICAL APPROACHES TO THE SELECTION OF THE MASTER THESES BY SPECIALTY 08.04.01 «CONSTRUCTION», PROFILE «TECHNOLOGY AND ORGANIZATION OF CONSTRUCTION»

The article considers methodological approaches to the selection of topics for final qualifying works in the form of master's theses in the direction of preparation 08.04.01 "Construction", the profile "Technologies and organization of construction". The authors justify the relevance of the topic based on the experience of training masters at the Department of construction production Technology of the Saint Petersburg state University of architecture and construction. The main requirements of modern professional standards for the formation of competencies of undergraduates are considered. The authors emphasize the importance of the first stage of research work in the semester, in which students together with the supervisor must identify the problem and determine the direction of work and formulate the topic of the final qualifying work (dissertation), based on the analysis of work experience and relevant documented and undocumented sources. Approaches to the formulation of research topics based on the identification of actual problems in various areas of technology and construction organization are proposed. The corresponding experience of work on the preparation of masters, accumulated at the Department, is analyzed. For this stage, a number of requirements for the selection of relevant research areas are presented and recommendations based on practical experience in this area are given. Various sources and directions of formulation of topics of graduate qualification works of undergraduates are analyzed. At the same time, the corresponding requirements for the student's scientific supervisor's experience are presented. In conclusion, the approaches to evaluating the work of undergraduates in the semester on the basis of a score-rating system, for the implementation of which a system for awarding points for various sections of their research, prepared articles, participation in conferences and case Championships is presented.
Fast Decryption: a New Feature of Misuse-Resistant AE

Misuse-resistant AE (MRAE) is a class of authenticated encryption (AE) that has a resistance against a potential misuse (repeat) of nonce. MRAE has received significant attention from the initial proposal by Rogaway and Shrimpton. They showed a generic MRAE construction called SIV. SIV becomes a de-facto scheme for MRAE, however, one notable drawback is its two-pass operation for both encryption and decryption. This implies that MRAE built on SIV is slower than the integrated nonce-based AE schemes, such as OCB.In this paper, we propose a new method to improve this situation. Particularly, our MRAE proposal (decryption-fast SIV or DFV) allows to decrypt as fast as a plain decryption, hence theoretically doubles its speed from the original SIV, while keeping the encryption speed equivalent to SIV. We present several generic compositions for DFV and their instantiations.
WhatsApp-Based Speaking Test in EFL Context

Prior studies had reported that the speaking test was quite troublesome. WhatsApp-based speaking test was one of the technology-enhanced assessment methods that became an option for more practical assessment. It was a descriptive qualitative study that was conducted by investigating 145 non-English university students in Indonesia. Data collecting instruments were observation, documentation, and open-ended questionnaires. Then, the data analysis used descriptions on each category. The result exposed that 50.3% of the overall student was somewhat satisfied with the implementation of the WhatsApp-based speaking test. The study reported five significant students’ positive responses to the test. Those positive responses were that it was considered as a fun-based test. It created an opportunity for the students to improve their language performance, it was also a time-saving test and it built a test environment that was less pressured and less anxious for the students. Then, the test was more appropriate for the students who belonged to the post-millennial generation. Also, the next finding of this study revealed three students’ negative responses towards the implementation of WhatsApp-based speaking tests in English classes. Those three prominent responses were the students disturbed by the noisy atmosphere during the test, the students encountered some technical problems on the test, and the students were still quite nervous during the test due to it was their first experience in doing the WhatsApp-based speaking test.
On Formative Evaluation System of College English Grading Teaching

To meet the requirements of college English teaching under the new situation and enhance the ability of college students to learn the English independently, modern information technology means is used to have a try and an experiment. English grading teaching is taken as the starting point, due to construct a mixed formative evaluation system, and provide a reference for improving the effects of college English teaching.
Sistem Pendukung Keputusan Rekrutmen Pada Perusahaan Outsourcing Berbasis Web

Computer technology and the internet do present many conveniences in business. Includes a web-based recruitment decision support system. In the current system, PT. X received a job application file sent to the office of PT. X, after that applicants await calls from PT. X work for the interview. Such conditions make the performance of PT. X work is considered ineffective and inefficient. Therefore we need a web-based employee recruitment system that can solve this problem. The method used in the construction of this system is the analytical method hierarchy process which is carried out in several stages, namely data collection, system requirements analysis, system analysis and design, system development, and ends with testing and revision. As a result, with a web-based recruitment system, the recruitment process is effective and efficient&#x0D; &#x0D; &#x0D; Keywords: Recruitment, Outsourcing, Employment, Decision Support Systems